Meet the Team: How Agentic AI Transforms Your Data Analysis
When you ask Querri a question about your data, you aren't just talking to a chatbot. You are deploying a coordinated team of specialized AI agents, each trained for a specific role in your data analysis pipeline.
Meet the Team: How Agentic AI Transforms Your Data Analysis
When you ask Querri a question about your data, you aren't just talking to a chatbot. You are deploying a coordinated team of specialized AI agents, each trained for a specific role in your data analysis pipeline.
Think of it less like using a software tool and more like walking into a well-organized consulting firm. You have specialists who clean up the mess, planners who organize the project, researchers who dig for context, and analysts who crunch the numbers. These agents collaborate, hand off tasks, and—crucially—check each other's work until they deliver insights you can actually use.
This multi-agent architecture (often called agentic AI) is the secret sauce behind Querri's ability to handle the messy reality of business data. It transforms raw, chaotic files into clear business intelligence, making your work repeatable, automatable, and deeply reliable.
Let's introduce you to the workforce living inside the Querri software.
Why One AI Isn't Enough for Data Analysis
You might wonder, "Why not just use one big AI model for everything?"
The answer comes down to specialization. A single monolithic AI can technically do many things, but it struggles with deep expertise across conflicting tasks.
Expertise: Our coding agent uses a model fine-tuned specifically for Python, while our visual reviewer uses a multimodal model that "sees" images to check charts.
Reliability: Smaller, focused agents are easier to debug. If file parsing breaks, we fix the preprocessing agent without touching the analysis code.
Efficiency: We don't need the most expensive, powerful brain for simple tasks. We optimize for speed and cost by assigning the right model to the right job.
By breaking the problem down, we create a system that is far more robust than a standard AI data analyst.
The File Whisperer: Cleaning Up the Mess
Data rarely arrives clean. You know the pain: CSV files with weird delimiters, Excel sheets with merged cells, or JSON responses nested three levels deep.
Enter The File Whisperer, our Preprocessing Agent.
This agent handles the "grunt work" that usually consumes hours of an analyst's time. It uses an iterative trial-and-error approach. It examines your file, makes educated guesses about its structure, tests those guesses, and refines them until the data loads cleanly.
If a standard approach fails, it doesn't give up; it switches to fallback strategies. You never see this complexity. You just upload a messy file and get a clean table.
What it handles:
Detecting tricky delimiters (pipes, tabs, semicolons)
Fixing encoding issues that turn text into gibberish
Flattening complex nested JSON files
Identifying header rows automatically
Click here to read more about AI for Data Cleaning.
The Cartographer: Mapping Your Spreadsheets
Complex Excel files are rarely simple lists. They often contain multiple tables on a single sheet—summary tables at the top, detail tables below, and lookup tables on the side.
The Cartographer (our Excel Region Agent) visually analyzes the sheet structure. It inspects cell ranges and identifies boundaries to extract each table separately. What takes a human analyst minutes of manual copying and pasting happens automatically in the background.
The Coordinator: Your Project Manager
Every conversation starts with The Coordinator. This Planner Agent acts as the project manager. It understands your natural-language question, assesses the data you have available, and decides the best approach to get answers.
Crucially, the Coordinator maintains context. If you ask a follow-up question, it knows exactly what data you were just looking at. If you request a modification, it understands what you've already built. It orchestrates the entire workflow, calling on specialist agents as needed while keeping the conversation on track.
The Research Assistant: Knowing Your Data
Before you can analyze data, you have to understand what is inside it. The Research Assistant (Examine Agent) maintains a mental map of your entire project—every table, column, and relationship.
It answers questions about structure without running expensive queries. Need column statistics? It provides them instantly. Want to know which steps produce which outputs? It knows immediately. It only generates SQL when you need actual aggregations, saving time and computing power.
The Analyst: Writing Code for You
This is where the insights are born. The Analyst is our Coder Agent.
Unlike basic chatbots that might hallucinate numbers, this agent writes actual Python code to analyze your data. It executes this code in a secure, sandboxed environment to return visualizations or transformed datasets.
Because it writes code, your analysis is:
Repeatable: You can run the same analysis on new data next month.
Transparent: You can see exactly how the answer was calculated.
Flexible: It uses powerful libraries like Pandas and Plotly for deep analysis.
But it doesn't stop at generating code. Every output goes through a Visual Review. Another AI examines the resulting charts to ensure they are readable and actually answer your question. If a chart looks cluttered or a calculation seems off, it automatically retries with a different approach.
The Librarian: Finding the Right Source
In many organizations, the problem isn't analyzing data—it's finding it. The Librarian (Find Sources Agent) searches through your entire data catalog to identify which datasets are relevant to your question.
It scans column names, types, and sample values to make a selection. It understands your organization's specific terminology and automatically loads the right data into your project, so you don't have to hunt for filenames.
The Researcher: Going Row-by-Row
Some questions can't be answered with simple math. If you need to classify customer sentiment, extract entities from text fields, or enrich data with external info, you need The Researcher.
This agent processes data row-by-row. It prevents expensive mistakes by showing you a preview—processing just 30 rows to demonstrate its plan—before you commit to processing the full dataset. It's perfect for tasks like sentiment classification or category assignment. This is just one example of how Querri's AI for data analysis handles complex data processing tasks.
To read more about the Researcher, read our January release notes.
Validate and Review: Ensuring Trustworthy Results
In real-world AI for data analysis, accuracy and trust are paramount. That's why every Querri agent doesn't just execute instructions—they constantly validate and review their outputs at every step of the pipeline.
Agents actively reason about their results, reviewing whether outputs align with expectations. For example, when generating Python code, an agent doesn't simply stop once the code runs; it performs an internal code review, checking the logic and cross-referencing the output against the original intent. When a visualization is created, it's visually inspected—not just for correctness, but for clarity, relevance, and interpretability. Plain English explanations are also generated and validated to reduce the risk of AI "hallucination" or misinterpretation.
This robust, layered approach means that every step is reviewed both logically and visually:
Code outputs are cross-checked to ensure they deliver logical and useful results.
Visualizations are inspected to confirm they accurately represent the data and answer the intended question.
Natural language summaries are validated to ensure they clearly explain findings in an accessible way.
And, of course, Querri's agents include built-in retry logic:
File parsing can try up to 10 different parameter combinations.
Code generation retries up to 7 times if errors are detected.
Visual review stops unreadable or misleading charts before they reach you.
Whenever an agent encounters an issue, it doesn't just retry blindly—it adjusts its reasoning, analyzes errors, and tries alternative strategies. This process includes error-specific guidance, such as handling tricky data conversions or resolving ambiguous delimiters. Validation happens at multiple levels, from initial data ingestion to the final result.
This comprehensive system means users receive not just fast analysis, but the most trustworthy answers AI can provide—reliable, transparent, and always reviewed before they reach your screen.
The Bottom Line
When you use Querri, you aren't just running queries. You are deploying a coordinated team of AI agents that understand data formats, write code, review their own work, and explain their findings in plain language.
This allows you to focus on the decisions that matter, rather than the tedious work of data preparation. It is decision support that works the way human teams work: through specialization, collaboration, and continuous improvement.
Ready to put your team to work? Get started for free and ask your first question today.